If a model systematically makes errors disproportionately for patients in the protected group, it is likely to lead to unequal outcomes. Equal performance refers to the assurance that a model is equally accurate for patients in the protec...

This paper proposes a new bias evaluation metric – Gender-based Illicit Proximity Estimate (GIPE), which measures the extent of undue proximity in word vectors resulting from the presence of gender-based predilections. Experiments based on a suite of...

We propose a criterion for discrimination against a specified sensitive attribute in supervised learning, where the goal is to predict some target based on available features. Assuming data about the predictor, target, and membership in the protected group...

We study fairness in classification, where individuals are classified, e.g., admitted to a university, and the goal is to prevent discrimination against individuals based on their membership in some group, while maintaining utility for the classifier (the ...

The scientific community is increasingly aware of the necessity to embrace pluralism and consistently represent major and minor social groups. Currently, there are no standard evaluation techniques for different types of biases. Accordingly, there is an urg...

Despite the success of deep neural networks (DNNs) in enabling on-device voice assistants, increasing evidence of bias and discrimination in machine learning is raising the urgency of investigating the fairness of these systems. Speaker verification is a fo...

We propose a set of interrelated metrics, all based on the notion of AI output concentration, and the related Lorenz curve/Lorenz area under the curve, able to measure the Sustainability/robustness, Accuracy, Fairness/privacy, Explainability/accountability ...

The demographic disparity metric (DD) determines whether a facet has a larger proportion of the rejected outcomes in the dataset than of the accepted outcomes. In the binary case where there are two facets, men and women for example, that constitute the dat...

The PGC metric compares the top-K ranking of features importance drawn from the entire dataset with the top-K ranking induced from specific subgroups of predictions. It can be applied to both categorical and regression problems, being useful for quantifying...

The metric GFIS is based on the concept of entropy. More precisely on the entropy of the normalized features measure, which represents the concentration of information within a set of features. Lower entropy values indicate that the majority of the explanat...

Machine learning models, at the core of AI applications,  typically achieve a high accuracy at the expense of an insufficient explainability. Moreover, according to the proposed regulations,  AI applications based on machine learning must be "trus...

In statistics, the Kendall rank correlation coefficient, commonly referred to as Kendall's τ coefficient, is a statistic used to measure the ordinal association between two measured quantities. A τ test is a non-parametric hypothesis test for statistical de...

In the field of health, equal patient outcomes refers to the assurance that protected groups have equal benefit in terms of patient outcomes from the deployment of machine-learning models. A weak form of equal outcomes is ensuring that both the protect...